Advanced Multimedia Processing (AMP) Lab

Computational Models of Kinship Verification

People

Abstract

Face recognition is an active research area in the computer vision community. Classical algorithms like Eigenface and Fisherface are known to provide a robust framework for face recognition, while some recent works address the illumination and orientation invariant recognition. However the recognition of people bonded by kinship using facial images is a marginally explored research field. This has led to computational models of kinship verification.

In this work, we present a computational model for kinship verification using novel feature extraction and selection methods, automatically
classifying pairs of face images as "related" or "unrelated"
(in terms of kinship). First, we conducted a controlled online
search to collect frontal face images of 150 pairs of public
figures and celebrities, along with images of their parents or
children. Next, we propose and evaluate a set of low-level
image features for this classification problem. After
selecting the most discriminative inherited facial features,
we demonstrate a classification accuracy of 70.67% on a test
set of image pairs using K-Nearest-Neighbors. Finally, we
present an evaluation of human performance on this
problem.

Dataset

We introduce the first comprehensive publicly available kinship database of 143 pairs of parents and children (300 cropped frontal face images of 100 by 100 pixels). Kinship Verfication. (Note that 7 families are removed from the original dataset of 150 families due to privacy issue.)

The images below gives a glimpse of the parents and children pairs in the dataset. Parents dataset on the left and children dataset on the right.

If you use the 'Kinship verfication dataset' in your research, please cite the following paper,